Difference between revisions of "stat946F18"

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(Record your contributions here [https://docs.google.com/spreadsheets/d/1SxkjNfhOg_eXWpUnVHuIP93E6tEiXEdpm68dQGencgE/edit?usp=sharing])
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[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary]
 
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/Hierarchical_Representations_for_Efficient_Architecture_Search Summary]
 
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|Oct 30 ||  Manpreet Singh Minhas || 1|| End-to-end Active Object Tracking via Reinforcement Learning || [http://proceedings.mlr.press/v80/luo18a/luo18a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=End_to_end_Active_Object_Tracking_via_Reinforcement_Learning Summary]
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|Oct 30 ||  Manpreet Singh Minhas || 4 || End-to-end Active Object Tracking via Reinforcement Learning || [http://proceedings.mlr.press/v80/luo18a/luo18a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=End_to_end_Active_Object_Tracking_via_Reinforcement_Learning Summary]
 
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|Oct 30 ||  Marvin Pafla || 2 || Fairness Without Demographics in Repeated Loss Minimization ||  [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Fairness_Without_Demographics_in_Repeated_Loss_Minimization Summary]
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|Oct 30 ||  Marvin Pafla || 5 || Fairness Without Demographics in Repeated Loss Minimization ||  [http://proceedings.mlr.press/v80/hashimoto18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Fairness_Without_Demographics_in_Repeated_Loss_Minimization Summary]
 
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|Oct 30 ||  Glen Chalatov || 3 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding Paper] ||
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|Oct 30 ||  Glen Chalatov || 6 || Pixels to Graphs by Associative Embedding || [http://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding Paper] ||
 
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pixels_to_Graphs_by_Associative_Embedding Summary]
 
[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Pixels_to_Graphs_by_Associative_Embedding Summary]
 
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|Nov 1 ||  Sriram Ganapathi Subramanian || 1||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/differentiableplasticity Summary]
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|Nov 1 ||  Sriram Ganapathi Subramanian || 7 ||Differentiable plasticity: training plastic neural networks with backpropagation || [http://proceedings.mlr.press/v80/miconi18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=stat946F18/differentiableplasticity Summary]
 
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|Nov 1 ||  Hadi Nekoei || 1|| Synthesizing Programs for Images using Reinforced Adversarial Learning ||  [http://proceedings.mlr.press/v80/ganin18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Synthesizing_Programs_for_Images_usingReinforced_Adversarial_Learning Summary]
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|Nov 1 ||  Hadi Nekoei || 8 || Synthesizing Programs for Images using Reinforced Adversarial Learning ||  [http://proceedings.mlr.press/v80/ganin18a.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Synthesizing_Programs_for_Images_usingReinforced_Adversarial_Learning Summary]
 
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|Nov 1 ||  Henry Chen || 1|| DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] ||  
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|Nov 1 ||  Henry Chen || 9 || DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks || [https://ieeexplore.ieee.org/abstract/document/7989236 Paper] ||  
 
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|Nov 6 ||  Nargess Heydari || 2 ||Wavelet Pooling For Convolutional Neural Networks Networks || [https://openreview.net/pdf?id=rkhlb8lCZ Paper] ||  
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|Nov 6 ||  Nargess Heydari || 10  ||Wavelet Pooling For Convolutional Neural Networks Networks || [https://openreview.net/pdf?id=rkhlb8lCZ Paper] ||  
 
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|Nov 6 ||  Aravind Ravi || 3 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] ||  
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|Nov 6 ||  Aravind Ravi || 11 || Towards Image Understanding from Deep Compression Without Decoding || [https://openreview.net/forum?id=HkXWCMbRW Paper] ||  
 
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|Nov 6 ||  Ronald Feng || 1 || Learning to Teach || [https://openreview.net/pdf?id=HJewuJWCZ Paper] ||  
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|Nov 6 ||  Ronald Feng || 12 || Learning to Teach || [https://openreview.net/pdf?id=HJewuJWCZ Paper] ||  
 
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|Nov 8 ||  Neel Bhatt || 1 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] ||  
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|Nov 8 ||  Neel Bhatt || 13 || Annotating Object Instances with a Polygon-RNN || [https://www.cs.utoronto.ca/~fidler/papers/paper_polyrnn.pdf Paper] ||  
 
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|Nov 8 ||  Jacob Manuel || 2 ||  ||  ||  
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|Nov 8 ||  Jacob Manuel || 14 ||  ||  ||  
 
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|Nov 8 ||  Charupriya Sharma|| 2 ||  ||  ||  
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|Nov 8 ||  Charupriya Sharma|| 15 ||  ||  ||  
 
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|NOv 13 || Sagar Rajendran  || 1|| Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] ||  
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|NOv 13 || Sagar Rajendran  || 16 || Zero-Shot Visual Imitation || [https://openreview.net/pdf?id=BkisuzWRW Paper] ||  
 
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|Nov 13 || Jiazhen Chen  || 2||  ||  ||  
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|Nov 13 || Jiazhen Chen  || 17 ||  ||  ||  
 
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|Nov 13 || Neil Budnarain  || 2|| PixelNN: Example-Based Image Synthesis  || [https://openreview.net/pdf?id=Syhr6pxCW Paper]  ||  
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|Nov 13 || Neil Budnarain  || 18 || PixelNN: Example-Based Image Synthesis  || [https://openreview.net/pdf?id=Syhr6pxCW Paper]  ||  
 
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|NOv 15 ||  Zheng Ma || 3|| Reinforcement Learning of Theorem Proving  ||  [https://arxiv.org/abs/1805.07563 Paper] ||  
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|NOv 15 ||  Zheng Ma || 19 || Reinforcement Learning of Theorem Proving  ||  [https://arxiv.org/abs/1805.07563 Paper] ||  
 
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|Nov 15 || Abdul Khader Naik  || 4||  ||  ||
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|Nov 15 || Abdul Khader Naik  || 20 ||  ||  ||
 
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|Nov 15 || Johra Muhammad Moosa  || 2|| Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper]  ||  
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|Nov 15 || Johra Muhammad Moosa  || 21 || Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin || [https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper]  ||  
 
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|NOv 20 || Zahra Rezapour Siahgourabi  || 1||  ||  ||  
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|NOv 20 || Zahra Rezapour Siahgourabi  || 22 ||  ||  ||  
 
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|Nov 20 || Shubham Koundinya  || 6||  ||  ||  
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|Nov 20 || Shubham Koundinya  || 23 ||  ||  ||  
 
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|Nov 20 || Salman Khan  || || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper]  ||  
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|Nov 20 || Salman Khan  || 24 || Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples || [http://proceedings.mlr.press/v80/athalye18a.html paper]  ||  
 
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|NOv 22 ||Soroush Ameli  || 3|| Learning to Navigate in Cities Without a Map ||  [https://arxiv.org/abs/1804.00168 paper] ||  
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|NOv 22 ||Soroush Ameli  || 25 || Learning to Navigate in Cities Without a Map ||  [https://arxiv.org/abs/1804.00168 paper] ||  
 
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|Nov 22 ||Ivan Li || 23 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] ||
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|Nov 22 ||Ivan Li || 26 || Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate || [https://arxiv.org/pdf/1806.05161v2.pdf Paper] ||
 
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|Nov 22 ||Sigeng Chen || 2 || || ||
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|Nov 22 ||Sigeng Chen || 27 || || ||
 
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|Nov 27 || Aileen Li  || 8|| Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper]    ||  
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|Nov 27 || Aileen Li  || 28 || Spatially Transformed Adversarial Examples ||[https://openreview.net/pdf?id=HyydRMZC- Paper]    ||  
 
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|NOv 27 ||Xudong Peng  || 9|| Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] ||  
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|NOv 27 ||Xudong Peng  || 29 || Multi-Scale Dense Networks for Resource Efficient Image Classification || [https://openreview.net/pdf?id=Hk2aImxAb Paper] ||  
 
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|Nov 27 ||Xinyue Zhang  || 10|| An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] ||  
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|Nov 27 ||Xinyue Zhang  || 30 || An Inference-Based Policy Gradient Method for Learning Options || [http://proceedings.mlr.press/v80/smith18a/smith18a.pdf Paper] ||  
 
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|NOv 29 ||Junyi Zhang  || 11||  Autoregressive Convolutional Neural Networks for Asynchronous Time Series  || [http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf Paper] ||
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|NOv 29 ||Junyi Zhang  || 31 ||  Autoregressive Convolutional Neural Networks for Asynchronous Time Series  || [http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf Paper] ||
 
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|Nov 29 ||Travis Bender  || 12|| Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper]  ||
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|Nov 29 ||Travis Bender  || 32 || Automatic Goal Generation for Reinforcement Learning Agents || [http://proceedings.mlr.press/v80/florensa18a/florensa18a.pdf Paper]  ||
 
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|Nov 29 ||Patrick Li  || 12|| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper]  ||
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|Nov 29 ||Patrick Li  || 33 || Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices || [https://www.cse.ust.hk/~huangzf/ICML18.pdf Paper]  ||
 
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|Makup || Ruijie Zhang || 1 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]||
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|Makeup || Ruijie Zhang || 34 || Searching for Efficient Multi-Scale Architectures for Dense Image Prediction || [https://arxiv.org/pdf/1809.04184.pdf Paper]||
 
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|Makup || Ahmed Afify || 2||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ  Paper]||
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|Makeup || Ahmed Afify || 35 ||Don't Decay the Learning Rate, Increase the Batch Size || [https://openreview.net/pdf?id=B1Yy1BxCZ  Paper]||
 
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|Makup || Gaurav Sahu  || 3 || TBD ||  ||
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|Makeup || Gaurav Sahu  || 36 || TBD ||  ||
 
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|Makup || Kashif Khan || 4 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper]  ||
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|Makeup || Kashif Khan || 37 || Wasserstein Auto-Encoders || [https://arxiv.org/pdf/1711.01558.pdf Paper]  ||
 
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|Makup || Shala Chen  || || A NEURAL REPRESENTATION OF SKETCH DRAWINGS ||  ||
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|Makeup || Shala Chen  || 38 || A NEURAL REPRESENTATION OF SKETCH DRAWINGS ||  ||
 
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|Makup || Ki Beom Lee || || || ||
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|Makeup || Ki Beom Lee || 39 || || ||
 
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|Makup || Wesley Fisher || || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Reinforcement_Learning_in_Continuous_Action_Spaces_a_Case_Study_in_the_Game_of_Simulated_Curling Summary]
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|Makeup || Wesley Fisher || 40 || Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling || [http://proceedings.mlr.press/v80/lee18b/lee18b.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Reinforcement_Learning_in_Continuous_Action_Spaces_a_Case_Study_in_the_Game_of_Simulated_Curling Summary]

Revision as of 11:55, 25 October 2018

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Your feedback on presentations


Record your contributions here [1]

Use the following notations:

P: You have written a summary/critique on the paper.

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Date Name Paper number Title Link to the paper Link to the summary
Feb 15 (example) Ri Wang Sequence to sequence learning with neural networks. Paper [Summary]
Oct 25 Dhruv Kumar 1 Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs Paper

Summary

Oct 25 Amirpasha Ghabussi 2 DCN+: Mixed Objective And Deep Residual Coattention for Question Answering Paper

Summary

Oct 25 Juan Carrillo 3 Hierarchical Representations for Efficient Architecture Search Paper

Summary

Oct 30 Manpreet Singh Minhas 4 End-to-end Active Object Tracking via Reinforcement Learning Paper Summary
Oct 30 Marvin Pafla 5 Fairness Without Demographics in Repeated Loss Minimization Paper Summary
Oct 30 Glen Chalatov 6 Pixels to Graphs by Associative Embedding Paper

Summary

Nov 1 Sriram Ganapathi Subramanian 7 Differentiable plasticity: training plastic neural networks with backpropagation Paper Summary
Nov 1 Hadi Nekoei 8 Synthesizing Programs for Images using Reinforced Adversarial Learning Paper Summary
Nov 1 Henry Chen 9 DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks Paper
Nov 6 Nargess Heydari 10 Wavelet Pooling For Convolutional Neural Networks Networks Paper
Nov 6 Aravind Ravi 11 Towards Image Understanding from Deep Compression Without Decoding Paper
Nov 6 Ronald Feng 12 Learning to Teach Paper
Nov 8 Neel Bhatt 13 Annotating Object Instances with a Polygon-RNN Paper
Nov 8 Jacob Manuel 14
Nov 8 Charupriya Sharma 15
NOv 13 Sagar Rajendran 16 Zero-Shot Visual Imitation Paper
Nov 13 Jiazhen Chen 17
Nov 13 Neil Budnarain 18 PixelNN: Example-Based Image Synthesis Paper
NOv 15 Zheng Ma 19 Reinforcement Learning of Theorem Proving Paper
Nov 15 Abdul Khader Naik 20
Nov 15 Johra Muhammad Moosa 21 Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin Paper
NOv 20 Zahra Rezapour Siahgourabi 22
Nov 20 Shubham Koundinya 23
Nov 20 Salman Khan 24 Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples paper
NOv 22 Soroush Ameli 25 Learning to Navigate in Cities Without a Map paper
Nov 22 Ivan Li 26 Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate Paper
Nov 22 Sigeng Chen 27
Nov 27 Aileen Li 28 Spatially Transformed Adversarial Examples Paper
NOv 27 Xudong Peng 29 Multi-Scale Dense Networks for Resource Efficient Image Classification Paper
Nov 27 Xinyue Zhang 30 An Inference-Based Policy Gradient Method for Learning Options Paper
NOv 29 Junyi Zhang 31 Autoregressive Convolutional Neural Networks for Asynchronous Time Series Paper
Nov 29 Travis Bender 32 Automatic Goal Generation for Reinforcement Learning Agents Paper
Nov 29 Patrick Li 33 Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices Paper
Makeup Ruijie Zhang 34 Searching for Efficient Multi-Scale Architectures for Dense Image Prediction Paper
Makeup Ahmed Afify 35 Don't Decay the Learning Rate, Increase the Batch Size Paper
Makeup Gaurav Sahu 36 TBD
Makeup Kashif Khan 37 Wasserstein Auto-Encoders Paper
Makeup Shala Chen 38 A NEURAL REPRESENTATION OF SKETCH DRAWINGS
Makeup Ki Beom Lee 39
Makeup Wesley Fisher 40 Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling Paper Summary